1 Objective

Update the parameters and equations used in Hectors forcing componet from the AR5 standards to the AR6 standards. I’ve started implementing these chages in Hector. Overall AR6 Hector output is much cooler than Hector 2.5 I will be going through the different forcing components to try and determine what is causing the change.

Up next I am going to try running Hector with the ssp to see how historical Hector compares with the RF values from AR6.

1.1 Overview

  • When we compare Hector v2.5 and Hector AR6 outputs with one another Hector AR6 is way too cool, I suspect that this has to do with the changes in how areosols are being modeled, there were significant changes & new ones were added.
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
library(hector)
library(data.table)
## 
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
## 
##     between, first, last
theme_set(theme_bw())

COLOR_THEME <- c("H-AR6" = "red", "H25" = "grey", "IPCC" = "black")

## library(hector) # note this is a speicifc branch krd_epa2  
DIR <- "/Users/dorh012/projects/2021/EPA_Hector" # define the analysis directory

# Hector 2.5 RCP 4.5 results 
read.csv(list.files(DIR, "Hector_25_rcp45.csv", full.names = TRUE)) %>% 
    select(scenario, year, variable, value, units)  -> 
    Hector_25_results

# Hector with new RF 
# we have to use a non standard ini file because we added NH3 to be consistent with AR6
ini <- file.path(DIR, "input", "hector_rcp45.ini") 
core <- newcore(ini, name = "H-AR6")
run(core)
## Hector core: H-AR6
## Start date:  1745
## End date:    2300
## Current date:    2300
## Input file:  /Users/dorh012/Documents/2021/EPA_Hector/input/hector_rcp45.ini
vars_to_save <- c(RF_CO2(), RF_N2O(),
                    RF_BC(), RF_OC(), RF_SO2(), 
                    RF_VOL(), RF_CH4(), GLOBAL_TEMP(), 
                    RF_CF4(), RF_C2F6(), RF_HFC23(), RF_HFC32(), RF_HFC4310(), 
                    RF_HFC125(), RF_HFC134A(), RF_HFC143A(), RF_HFC227EA(), 
                    RF_HFC245FA(), RF_SF6(), RF_CFC11(), RF_CFC12(), RF_CFC113(),
                    RF_CFC114(), RF_CFC115(), RF_CCL4(), RF_CH3CCL3(), RF_HCFC22(), 
                    RF_HCFC141B(), RF_HCFC142B(), RF_HALON1211(), RF_HALON1301(),
                    RF_HALON2402(), RF_CH3CL(), RF_CH3BR(), RF_TOTAL(), RF_NH3(),
                  EMISSIONS_CH4(), ATMOSPHERIC_CH4(), HEAT_FLUX())
Hector_AR6_results <- fetchvars(core, 
                                dates = 1750:2100, 
                                vars = vars_to_save)

data <- rbind(Hector_25_results, Hector_AR6_results)
# Hector 2.5 RCP 4.5 results 
read.csv(list.files(DIR, "Hector_25_rcp45-const.csv", full.names = TRUE)) %>% 
    select(scenario, year, variable, value, units)  -> 
    Hector_25_results_const


# Hector with new RF 
# we have to use a non standard ini file because we added NH3 to be consistent with AR6
ini <- file.path(DIR, "input", "hector_const.ini") 
core <- newcore(ini, name = "H-AR6")
run(core)
## Hector core: H-AR6
## Start date:  1745
## End date:    2300
## Current date:    2300
## Input file:  /Users/dorh012/Documents/2021/EPA_Hector/input/hector_const.ini
vars_to_save <- c(RF_CO2(), RF_TOTAL(), GLOBAL_TEMP())
Hector_AR6_results_const <- fetchvars(core, 
                                dates = 1750:2100, 
                                vars = vars_to_save)

data_const <- rbind(Hector_25_results_const, Hector_AR6_results_const)

2 Comparing Hector V25 with Hector AR6

2.1 Total RF

bind_rows(Hector_25_results, Hector_AR6_results) %>%  
    filter(variable == RF_TOTAL()) %>% 
    ggplot(aes(year, value, color = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable) + 
  scale_color_manual(values = COLOR_THEME)

2.2 Ocean Heat Flux

data %>% 
  filter(variable == HEAT_FLUX()) %>% 
    ggplot(aes(year, value, color = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable) + 
  scale_color_manual(values = COLOR_THEME)

2.3 Global Temp

bind_rows(Hector_25_results, Hector_AR6_results) %>%  
    filter(variable == GLOBAL_TEMP()) %>% 
    ggplot(aes(year, value, color = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)+ 
  scale_color_manual(values = COLOR_THEME)

2.4 Major GHGs

2.4.1 CO2

So because CO2 RF is so important & is affected by the temperature feedbacks we have to do this comparison with the CO2 constraints on (no temperature carbon feedbacks on). This is the pattern of change we expected from the inital comparison that calulated RF based on Hector outputs.

IPCC text says that CO2 RF is higher, which is consistent with what we see here.

data_const %>% 
    filter(variable == RF_CO2()) %>% 
    ggplot(aes(year, value, color = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable) + 
  labs(title = "Hector with CO2 constraints") + 
  scale_color_manual(values = COLOR_THEME)

2.4.2 CH4

Text says the changes in CH4 RF should increase between AR5 & AR6, consistent with the changes in Hector CH4 RF.

# KALYN may want to do this with constrainted CO2!!
data %>% 
    filter(variable == RF_CH4()) %>% 
    ggplot(aes(year, value, color = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable) + 
  scale_color_manual(values = COLOR_THEME)

2.4.3 N2O

Text says the changes in N2O RF should increase between AR5 & AR6, consistent with the changes in Hector N2O RF.

data %>% 
    filter(variable == RF_N2O()) %>% 
    ggplot(aes(year, value, color = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable) + 
  scale_color_manual(values = COLOR_THEME)

2.5 Aerosols

2.5.1 BC

STEVE - line 46 of 7-34 is BC RF supposed to weaken & OC strengthen?

data %>% 
    filter(variable == RF_BC()) %>% 
    ggplot(aes(year, value, color = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable) + 
  scale_color_manual(values = COLOR_THEME)

2.5.2 OC

data %>% 
    filter(variable == RF_OC()) %>% 
    ggplot(aes(year, value, color = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable) + 
  scale_color_manual(values = COLOR_THEME)

2.5.3 SO2

bind_rows(filter(Hector_25_results, variable %in% c("FSO2d", "FSO2", "FSO2i")),
          filter(Hector_AR6_results, variable %in% c(RF_SO2(), RF_ACI()))) %>% 
    ggplot(aes(year, value, color = variable)) + 
    geom_line() + 
    facet_grid(units ~ scenario)

2.5.4 NH3

data %>% 
  filter(variable == RF_NH3()) %>%  
    ggplot(aes(year, value, color = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable) + 
  labs(title = "Notes: Hector 2.5 does include NH3 RF") + 
  scale_color_manual(values = COLOR_THEME)

2.6 Halocarbons

data %>% 
  filter(variable ==  "FCF4") %>%  
    ggplot(aes(year, value, color = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  + 
  scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FC2F6") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FHFC23") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FHFC32") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FHFC4310") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FHFC125") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FHFC134a") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FHFC143a") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FHFC227ea") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FHFC245fa") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FSF6") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FCFC11") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FCFC12") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FCFC114") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FCFC115") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FCCl4") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FCH3CCl3") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FHCFC22") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FHCFC141b") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FHCFC142b") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "Fhalon1211") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "Fhalon1301") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "Fhalon2402") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FCH3Cl") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

data %>% 
  filter(variable ==  "FCH3Br") %>%  
    ggplot(aes(year, value, color = scenario, linetype = scenario)) + 
    geom_line() + 
    facet_grid(units ~ variable)  +    scale_color_manual(values = COLOR_THEME)

3 Comparing with IPCC Results

In this section we compare IPCC RF results with output from the Hector V3 (using AR6 RF information). In most cases the IPCC results will have three lines a central output with a 5 and 95% output.

Load the IPCC AR6 data.

format_data <- function(path){
  
  x <- gsub(pattern = ".csv", replacement = "", x = basename(path))
  xx <- unlist(strsplit(x, split = "_"))
  
  if (length(xx) == 3 ){
    out <- as.data.frame(matrix(xx, nrow = 1, dimnames = list(NULL, c("output", "scn", "time"))))
  }
  
  if (length(xx) == 4 ){
    out <- as.data.frame(matrix(xx, nrow = 1, dimnames = list(NULL, c("output", "scn", "time", "percent"))))
  }
  
  return(out)
  
}

get_cmip_data <- function(path){
  
  dat <- read.csv(path, stringsAsFactors = FALSE)
  info <- format_data(path)
  
  dat %>% 
    melt(id.vars = c( "year"), 
         variable.name = "variable", value.name = "value") %>% 
    cbind(info) -> 
    out
  
  return(out)
}

AR6_dir <- "/Users/dorh012/Documents/2021/Chapter-7/data_output"
files <- list.files(file.path(AR6_dir,"SSPs"), pattern = "ERF", full.names = TRUE)  

files %>% 
  lapply(get_cmip_data) %>% 
  bind_rows() %>% 
  select(year, variable, value, scenario = scn, percent) -> 
  ar6_data

ar6_names <- c("co2","ch4","n2o","o3","h2o_stratospheric","aerosol.cloud_interactions",
               "volcanic","total", "HFC.134a","HFC.23","HFC.32","HFC.125","HFC.143a", 
               "HFC.227ea", "HFC.245fa", "SF6","CF4","CFC.11", "CFC.113", 
               "CFC.114", "CFC.115")
hector_names <- c(RF_CO2(), RF_CH4(), RF_N2O(), RF_O3_TROP(), RF_H2O_STRAT(), RF_ACI(),
                  RF_VOL() ,RF_TOTAL(), RF_HFC134A(),RF_HFC23(), RF_HFC32(), RF_HFC125(),
                  RF_HFC143A(), RF_HFC227EA(), RF_HFC245FA(), RF_SF6(), RF_CF4(),
                  RF_CFC11(), RF_CFC113(), RF_CFC114(), RF_CFC115()) 

ar6_data %>% 
  filter(variable %in% ar6_names) %>% 
  left_join(data.frame(variable = ar6_names, 
                       hvar = hector_names)) %>% 
  select(year, variable = hvar, value, scenario, percent) -> 
  ar6_results

Run Hector(with AR6 forcing) with the ssp scenanrios.

run_hector_ssp <- function(ini){
    
    hector_names <- c(RF_CO2(),RF_CH4(),RF_N2O(),RF_O3_TROP(),RF_H2O_STRAT(),RF_ACI(),RF_VOL(),RF_TOTAL(),
                      RF_HFC134A(),RF_HFC23(),
                      RF_HFC32(),RF_HFC125(),RF_HFC143A(),RF_HFC227EA(),RF_HFC245FA(),RF_SF6(),RF_CF4(),RF_CFC11(),
                      RF_CFC113(),
                      RF_CFC114(),RF_CFC115(), RF_BC(), RF_SO2(), RF_OC(), RF_NH3()) 
    
    name <- gsub(pattern = "hector_|.ini", replacement = "", x = basename(ini))
    core <- newcore(ini, name = name)
    run(core, runtodate = 2050)
    out <- fetchvars(core, 1750:2100, hector_names)
    
    return(out)
    
}


list.files("/Users/dorh012/projects/2021/EPA_Hector/input", pattern = "ini", full.names = TRUE) %>% 
    lapply(run_hector_ssp) %>% 
    bind_rows() ->
    hector_out
hector_out$source <- 'H-AR6'
ar6_results$source <- 'IPCC'
comp_data <- bind_rows(ar6_results, hector_out) %>% 
  filter(scenario %in% c("ssp119", "ssp126", "ssp245"))

3.1 Total

var <- RF_TOTAL()
comp_data %>% 
  filter(variable == var) %>% 
  filter(year <= 2050) %>% 
  ggplot(aes(year, value, color = source, line = percent)) + 
  geom_line() +
  facet_wrap("scenario") + 
  labs(title = var) + 
  scale_color_manual(values = COLOR_THEME)

3.2 CO2

var <- RF_CO2()
comp_data %>% 
  filter(variable == var) %>% 
  filter(year <= 2050) %>% 
  ggplot(aes(year, value, color = source, line = percent)) + 
  geom_line() +
  facet_wrap("scenario") + 
  labs(title = var, subtitle = 'Hector is emission driven') + 
  scale_color_manual(values = COLOR_THEME)

var <- RF_CO2()
comp_data %>% 
  filter(variable == var & source == "IPCC") %>% 
  filter(year <= 2050) %>% 
  filter(scenario %in% c("ssp119", "ssp245")) -> 
  IPCC_data 

hector_out %>% 
  filter(scenario %in% c("ssp119_const", "ssp245_const")) %>% 
  mutate(scenario = gsub(scenario, pattern = "_const", replacement = "")) %>% 
  filter(year <= 2050) %>% 
  filter(variable == var) %>% 
  mutate(source = "H-AR6") -> 
  hector_const_co2

bind_rows(hector_const_co2, IPCC_data) %>% 
  ggplot(aes(year, value, color = source, line = percent)) + 
  geom_line(alpha = 0.5) +
  facet_wrap("scenario") + 
  labs(title = var, subtitle = 'Hector is CO2 conc driven') + 
  scale_color_manual(values = COLOR_THEME)

Awesome! When CO2 concenatrions are constrained Hector CO2 RF hits the IPCC MEAN! whoot whoot

3.3 N2O

var <- RF_N2O()
comp_data %>% 
  filter(variable == var) %>% 
  filter(year <= 2050) %>% 
  ggplot(aes(year, value, color = source, line = percent)) + 
  geom_line() +
  facet_wrap("scenario") + 
  labs(title = var) + 
  scale_color_manual(values = COLOR_THEME)

var <- RF_N2O()
comp_data %>% 
  filter(variable == var & source == "IPCC") %>% 
  filter(year <= 2050) %>% 
  filter(scenario %in% c("ssp119", "ssp245")) -> 
  IPCC_data 

hector_out %>% 
  filter(scenario %in% c("ssp119_const", "ssp245_const")) %>% 
  mutate(scenario = gsub(scenario, pattern = "_const", replacement = "")) %>% 
  filter(year <= 2050) %>% 
  filter(variable == var) %>% 
  mutate(source = "H-AR6") -> 
  hector_const_co2

bind_rows(hector_const_co2, IPCC_data) %>% 
  ggplot(aes(year, value, color = source, line = percent)) + 
  geom_line(alpha = 0.5) +
  facet_wrap("scenario") + 
  labs(title = var, subtitle = 'Hector is N2O conc driven') + 
  scale_color_manual(values = COLOR_THEME)

3.4 CH4

var <- RF_CH4()
comp_data %>% 
  filter(variable == var) %>% 
  filter(year <= 2050) %>% 
  ggplot(aes(year, value, color = source, line = percent)) + 
  geom_line() +
  facet_wrap("scenario") + 
  labs(title = var) + 
  scale_color_manual(values = COLOR_THEME) 

var <- RF_CH4()
comp_data %>% 
  filter(variable == var & source == "IPCC") %>% 
  filter(year <= 2050) %>% 
  filter(scenario %in% c("ssp119", "ssp245")) -> 
  IPCC_data 

hector_out %>% 
  filter(scenario %in% c("ssp119_const", "ssp245_const")) %>% 
  mutate(scenario = gsub(scenario, pattern = "_const", replacement = "")) %>% 
  filter(year <= 2050) %>% 
  filter(variable == var) %>% 
  mutate(source = "H-AR6") -> 
  hector_const_co2

bind_rows(hector_const_co2, IPCC_data) %>% 
  ggplot(aes(year, value, color = source, line = percent)) + 
  geom_line(alpha = 0.5) +
  facet_wrap("scenario") + 
  labs(title = var, subtitle = 'Hector is CH4 conc driven') + 
  scale_color_manual(values = COLOR_THEME)

3.5 Fvol

var <- "Fvol"
comp_data %>% 
  filter(variable == var) %>% 
  filter(year <= 2050) %>% 
  ggplot(aes(year, value, color = source, line = percent)) + 
  geom_line(alpha = 0.5) +
  facet_wrap("scenario") + 
  labs(title = var) + 
  scale_color_manual(values = COLOR_THEME)

3.6 Aerosol Cloud Interactions

var <-  RF_ACI()
comp_data %>% 
  filter(variable == var) %>% 
  filter(year <= 2050) %>% 
  ggplot(aes(year, value, color = source, line = percent)) + 
  geom_line() +
  facet_wrap("scenario") + 
  labs(title = var) + 
  scale_color_manual(values = COLOR_THEME) 

# var <-  RF_ACI()
# comp_data %>% 
#   filter(variable == var) %>% 
#   filter(year %in% 2005:2014)  %>% 
#   group_by(scenario, variable, percent) %>% 
#   summarise(value = mean(value))

3.7 aerosol.radiation_interactions

hector_out %>% 
  filter(variable %in% c(RF_BC(), RF_OC(), RF_NH3(), RF_SO2())) %>% 
  group_by(year, scenario) %>% 
  summarise(value = sum(value)) %>% 
  mutate(variable = 'aerosol.radiation_interactions') %>% 
  mutate(source = "H-AR6") -> 
  hector_ari
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
ar6_data %>%
  filter(scenario %in% c("ssp119", "ssp126", "ssp245")) %>% 
  filter(variable == 'aerosol.radiation_interactions') %>% 
  mutate(source = "IPCC") -> 
  ipcccc_ari

bind_rows(hector_ari, ipcccc_ari) %>% 
  filter(scenario %in% c("ssp119", "ssp126", "ssp245")) %>% 
  filter(year <= 2050) %>% 
  ggplot(aes(year, value, color = source, line = percent)) + 
  geom_vline(xintercept = 2015)+
  geom_line() +
  facet_wrap("scenario") + 
  labs(title = "aerosol.radiation_interactions") + 
  scale_color_manual(values = COLOR_THEME)

3.8 FO3_trop

var <-"FO3_trop"
comp_data %>% 
  filter(variable == var) %>% 
  filter(year <= 2050) %>% 
  ggplot(aes(year, value, color = source, line = percent)) + 
  geom_line() +
  facet_wrap("scenario") + 
  labs(title = var) + 
  scale_color_manual(values = COLOR_THEME)

3.9 “FH2O_strat”

var <- "FH2O_strat"
comp_data %>% 
  filter(variable == var) %>% 
  filter(year <= 2050) %>% 
  ggplot(aes(year, value, color = source, line = percent)) + 
  geom_line() +
  facet_wrap("scenario") + 
  labs(title = var) + 
  scale_color_manual(values = COLOR_THEME)

4 Outstanding Issues

  • Hector N2O RF is funky compared with the IPCC report
  • CH4 RF during the historical period is odd – I think it is related to the way Hecctor is computing the concentrations